This project analyzes the energy consumption of software polar decoders on low power processors, providing insights to optimize energy efficiency and monitor performance. It helps in making informed decisions for better energy management and improving the overall system's performance.

This project focuses on improving the performance of software by analyzing and optimizing code. It provides tools for profiling and benchmarking algorithms, and offers recommendations for improving performance. The project is designed to help developers write more efficient code and improve the overall performance of their software.

Energy Aware Runtime (EAR) is an open-source software that provides an energy management framework for supercomputers. It includes an easy-to-use optimization service that automatically selects the optimal CPU frequency based on the application and node characteristics. It also includes a complete energy and performance accounting and monitoring system based on SQL database, and a global energy management system to monitor and control the energy consumed in the system.

StarPU is a open-source software framework that enables the optimization of scientific computing applications for heterogeneous architectures. It provides a simple and efficient way to parallelize compute-intensive tasks and manage resources, resulting in improved performance and reduced execution time.

A tool to count the number of loops that can be approximated using Approximated Unrolling, helping to optimize software performance and analyze code for potential improvements.